Pub Date : 2011-06-27DOI: 10.1109/CBMS.2011.5999022
T. Solomonides
The 24th International Symposium on Computer-Based Medical Systems, CBMS 2011, took place at the University of the West of England, Bristol, UK, on 27th to 30th June 2011. As a special feature, instead of the traditional (since 2005) special track on “healthgrids”, i.e. grid computing for biomedicine and healthcare, latterly encompassing cloud computing also, the conference HealthGrid 2011 colocated with CBMS to the benefit of both. This was the culmination of a hope that those of us working at UWE had entertained since 2008. The invitation to CBMS was first made in Jyvaskyla in 2008, became a formal proposal in Albuquerque in 2009 and was confirmed in Perth in 2010. As for HealthGrid, it seemed an opportunity not to be missed to colocate with CBMS in Bristol, only the second time the conference has been awarded to a British city (after Oxford in 2005).
{"title":"Proceedings of the 24th International Symposium on Computer-Based Medical Systems - CBMS 2011 Bristol, UK","authors":"T. Solomonides","doi":"10.1109/CBMS.2011.5999022","DOIUrl":"https://doi.org/10.1109/CBMS.2011.5999022","url":null,"abstract":"The 24th International Symposium on Computer-Based Medical Systems, CBMS 2011, took place at the University of the West of England, Bristol, UK, on 27th to 30th June 2011. As a special feature, instead of the traditional (since 2005) special track on “healthgrids”, i.e. grid computing for biomedicine and healthcare, latterly encompassing cloud computing also, the conference HealthGrid 2011 colocated with CBMS to the benefit of both. This was the culmination of a hope that those of us working at UWE had entertained since 2008. The invitation to CBMS was first made in Jyvaskyla in 2008, became a formal proposal in Albuquerque in 2009 and was confirmed in Perth in 2010. As for HealthGrid, it seemed an opportunity not to be missed to colocate with CBMS in Bristol, only the second time the conference has been awarded to a British city (after Oxford in 2005).","PeriodicalId":74567,"journal":{"name":"Proceedings. IEEE International Symposium on Computer-Based Medical Systems","volume":"96 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2011-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78767698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Classification methods are widely used in computer-based medical systems. Often, the accuracy of a classifier can be improved using a classifier ensemble, the combination of several classifiers. Two classifiers ensembles and their results on several medical data sets will be presented: Rotation Forest (Rodriguez, Kuncheva and Alonso) and Random Oracles (Kuncheva and Rodriguez). Rotation Forest is a method for generating classifier ensembles based on feature extraction. To create the training data for a base classifier, the feature set is randomly split into K subsets (K is a parameter of the algorithm) and Principal Component Analysis (PCA) is applied to each subset. All principal components are retained in order to preserve the variability information in the data. Thus, K axis rotations take place to form the new features for a base classifier. The idea of the rotation approach is to encourage simultaneously individual accuracy and diversity within the ensemble. Diversity is promoted through the feature extraction for each base classifier. Decision trees were chosen here because they are sensitive to rotation of the feature axes, hence the name "forest." Accuracy is sought by keeping all principal components and also using the whole data set to train each base classifier. Comparisons with various standard ensemble methods (Bagging, AdaBoost, and Random Forest) will be reported. Diversity-error diagrams reveal that Rotation Forest ensembles construct individual classifiers which are more accurate than these in AdaBoost and Random Forest and more diverse than these in Bagging, sometimes more accurate as well. A random oracle classifier is a mini-ensemble formed by a pair of classifiers and a fixed, randomly created oracle that selects between them. The random oracle can be thought of as a random discriminant function which splits the data into two subsets with no regard of any class labels or cluster structure. Two random oracles has been considered: linear and spherical. A random oracle classifier can be used as the base classifier of any ensemble method. It is argued that this approach encourages extra diversity in the ensemble while allowing for high accuracy of the individual ensemble members. Experiments with several data sets from UCI and 11 ensemble models will be reported. Each ensemble model will be examined with and without the oracle. The results will show that all ensemble methods benefited from the new approach, most markedly so random subspace and bagging. A further experiment with seven real medical data sets will demonstrate the validity of these findings outside the UCI data collection. When using Naive Bayes Classifiers as base classifiers, the experiments show that ensembles based solely upon the spherical oracle (and no other ensemble heuristic) outrank Bagging, Wagging, Random Subspaces, AdaBoost.Ml, MultiBoost and Decorate. Moreover, all these ensemble methods are better with any of the two random oracles than their standard
分类方法广泛应用于基于计算机的医疗系统中。通常，可以使用分类器集成(多个分类器的组合)来提高分类器的准确性。将介绍两种分类器集合及其在若干医疗数据集上的结果:轮换森林(Rodriguez, Kuncheva和Alonso)和随机预言器(Kuncheva和Rodriguez)。旋转森林是一种基于特征提取的分类器集成生成方法。为了创建基分类器的训练数据，将特征集随机分成K个子集(K是算法的一个参数)，并对每个子集应用主成分分析(PCA)。为了保留数据中的变异性信息，保留了所有主成分。因此，发生K轴旋转以形成基本分类器的新特征。旋转方法的想法是同时鼓励个人的准确性和多样性在整体。通过对每个基分类器的特征提取来提升多样性。这里选择决策树是因为它们对特征轴的旋转很敏感，因此被称为“森林”。准确性是通过保留所有主成分和使用整个数据集来训练每个基分类器来寻求的。将报告与各种标准集成方法(Bagging, AdaBoost和Random Forest)的比较。多样性误差图显示，旋转森林集成构建的单个分类器比AdaBoost和Random Forest中的分类器更准确，比Bagging中的分类器更多样化，有时也更准确。随机oracle分类器是由一对分类器和一个固定的、随机创建的、在它们之间进行选择的oracle组成的小型集合。随机oracle可以被认为是一个随机判别函数，它将数据分成两个子集，而不考虑任何类标签或聚类结构。考虑了两种随机的神谕:线性的和球形的。随机oracle分类器可以作为任何集成方法的基础分类器。有人认为，这种方法鼓励了集合中额外的多样性，同时允许单个集合成员的高精度。本文将报道使用来自UCI和11个集成模型的几个数据集的实验。每个集成模型将在有或没有oracle的情况下进行检查。结果表明，所有的集成方法都受益于新方法，其中最明显的是随机子空间和套袋。对七个真实医疗数据集的进一步实验将证明这些发现在UCI数据收集之外的有效性。当使用朴素贝叶斯分类器作为基本分类器时，实验表明，仅基于球形预测(而没有其他集成启发式)的集成优于Bagging, Wagging, Random Subspaces, AdaBoost。Ml，多重增强和装饰。此外，所有这些集成方法使用任意两种随机oracle都比不使用oracle的标准版本要好。
{"title":"Rotation Forest and Random Oracles: Two Classifier Ensemble Methods","authors":"Juan José Rodríguez Diez","doi":"10.1109/CBMS.2007.94","DOIUrl":"https://doi.org/10.1109/CBMS.2007.94","url":null,"abstract":"Classification methods are widely used in computer-based medical systems. Often, the accuracy of a classifier can be improved using a classifier ensemble, the combination of several classifiers. Two classifiers ensembles and their results on several medical data sets will be presented: Rotation Forest (Rodriguez, Kuncheva and Alonso) and Random Oracles (Kuncheva and Rodriguez). Rotation Forest is a method for generating classifier ensembles based on feature extraction. To create the training data for a base classifier, the feature set is randomly split into K subsets (K is a parameter of the algorithm) and Principal Component Analysis (PCA) is applied to each subset. All principal components are retained in order to preserve the variability information in the data. Thus, K axis rotations take place to form the new features for a base classifier. The idea of the rotation approach is to encourage simultaneously individual accuracy and diversity within the ensemble. Diversity is promoted through the feature extraction for each base classifier. Decision trees were chosen here because they are sensitive to rotation of the feature axes, hence the name \"forest.\" Accuracy is sought by keeping all principal components and also using the whole data set to train each base classifier. Comparisons with various standard ensemble methods (Bagging, AdaBoost, and Random Forest) will be reported. Diversity-error diagrams reveal that Rotation Forest ensembles construct individual classifiers which are more accurate than these in AdaBoost and Random Forest and more diverse than these in Bagging, sometimes more accurate as well. A random oracle classifier is a mini-ensemble formed by a pair of classifiers and a fixed, randomly created oracle that selects between them. The random oracle can be thought of as a random discriminant function which splits the data into two subsets with no regard of any class labels or cluster structure. Two random oracles has been considered: linear and spherical. A random oracle classifier can be used as the base classifier of any ensemble method. It is argued that this approach encourages extra diversity in the ensemble while allowing for high accuracy of the individual ensemble members. Experiments with several data sets from UCI and 11 ensemble models will be reported. Each ensemble model will be examined with and without the oracle. The results will show that all ensemble methods benefited from the new approach, most markedly so random subspace and bagging. A further experiment with seven real medical data sets will demonstrate the validity of these findings outside the UCI data collection. When using Naive Bayes Classifiers as base classifiers, the experiments show that ensembles based solely upon the spherical oracle (and no other ensemble heuristic) outrank Bagging, Wagging, Random Subspaces, AdaBoost.Ml, MultiBoost and Decorate. Moreover, all these ensemble methods are better with any of the two random oracles than their standard ","PeriodicalId":74567,"journal":{"name":"Proceedings. IEEE International Symposium on Computer-Based Medical Systems","volume":"178 1","pages":"3"},"PeriodicalIF":0.0,"publicationDate":"2007-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82993115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2004-01-01DOI: 10.1109/CBMS.2004.1311683
S. Henrard
NIST has devised preliminary elements (technical “hooks”) of a convenient logging method for Web-based electronic health record (EHR) dialogues. These can identify fields, record times spent at each (by whomever), and log a sequence of visits. The next step will be to refine this promising start, to begin building upon it a more polished and user-friendly system. We present our results to gain impressions from users of the worth of simple, open tools for tuning and improving e-record flows and their corresponding with practice workflows.
{"title":"Preliminary Instrumentation for the Efficient Use of Web-Based Electronic Health Records","authors":"S. Henrard","doi":"10.1109/CBMS.2004.1311683","DOIUrl":"https://doi.org/10.1109/CBMS.2004.1311683","url":null,"abstract":"NIST has devised preliminary elements (technical “hooks”) of a convenient logging method for Web-based electronic health record (EHR) dialogues. These can identify fields, record times spent at each (by whomever), and log a sequence of visits. The next step will be to refine this promising start, to begin building upon it a more polished and user-friendly system. We present our results to gain impressions from users of the worth of simple, open tools for tuning and improving e-record flows and their corresponding with practice workflows.","PeriodicalId":74567,"journal":{"name":"Proceedings. IEEE International Symposium on Computer-Based Medical Systems","volume":"53 1","pages":"10-14"},"PeriodicalIF":0.0,"publicationDate":"2004-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75255711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2003-06-26DOI: 10.1109/CBMS.2003.1212770
Tan Yiyu, Zhang Ning
B mode ultrasonic ophthalmological scanners are used widely in clinic diagnosing of eye diseases. Digital scan converter (DSC) is a key component in their image processing systems, which performs coordinate transformation and image interpolation between two adjacent scan lines. We present a new image processing system scheme based on the FPGA technology. In this scheme, we mainly focus on the DSC component. In the DSC system, we applied the CORDIC algorithm to perform the transformation between Cartesian coordinates and polar coordinates, and use a modified R-theta algorithm to perform the image interpolations. At the same time, we mapped the whole system into a Xilinx FPGA device (XCV50-BG256). Its performance in practical application indicates that the scheme is valid and can improve the image quality significantly and greatly reduce the dimensions of this instrument.
{"title":"An Image Processing System Scheme in B Mode Ultrasonic Ophthalmological Scanner","authors":"Tan Yiyu, Zhang Ning","doi":"10.1109/CBMS.2003.1212770","DOIUrl":"https://doi.org/10.1109/CBMS.2003.1212770","url":null,"abstract":"B mode ultrasonic ophthalmological scanners are used widely in clinic diagnosing of eye diseases. Digital scan converter (DSC) is a key component in their image processing systems, which performs coordinate transformation and image interpolation between two adjacent scan lines. We present a new image processing system scheme based on the FPGA technology. In this scheme, we mainly focus on the DSC component. In the DSC system, we applied the CORDIC algorithm to perform the transformation between Cartesian coordinates and polar coordinates, and use a modified R-theta algorithm to perform the image interpolations. At the same time, we mapped the whole system into a Xilinx FPGA device (XCV50-BG256). Its performance in practical application indicates that the scheme is valid and can improve the image quality significantly and greatly reduce the dimensions of this instrument.","PeriodicalId":74567,"journal":{"name":"Proceedings. IEEE International Symposium on Computer-Based Medical Systems","volume":"3 1","pages":"74-79"},"PeriodicalIF":0.0,"publicationDate":"2003-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74407063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2003-06-26DOI: 10.1109/CBMS.2003.1212775
Yang Xiang, Qiwei Gu, Zhengxiang Li
A distributed telemedicine system is superior to isolated one. As a typical example, separating Web servers and data servers by CORBA technique can expand basic Web-based telemedicine system into a distributed system. As database would be fragmented on distributed sites, query optimization should be employed when possible. In addition, by estimating the resource utilization ratio, it is easy to achieve maximal profits for the distributed telemedicine system.
{"title":"A Distributed Framework of Web-Based Telemedicine System","authors":"Yang Xiang, Qiwei Gu, Zhengxiang Li","doi":"10.1109/CBMS.2003.1212775","DOIUrl":"https://doi.org/10.1109/CBMS.2003.1212775","url":null,"abstract":"A distributed telemedicine system is superior to isolated one. As a typical example, separating Web servers and data servers by CORBA technique can expand basic Web-based telemedicine system into a distributed system. As database would be fragmented on distributed sites, query optimization should be employed when possible. In addition, by estimating the resource utilization ratio, it is easy to achieve maximal profits for the distributed telemedicine system.","PeriodicalId":74567,"journal":{"name":"Proceedings. IEEE International Symposium on Computer-Based Medical Systems","volume":"27 1","pages":"108-"},"PeriodicalIF":0.0,"publicationDate":"2003-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79101654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2003-01-01DOI: 10.1109/CBMS.2003.1212808
Tao Xu, Hancheng Xing
{"title":"Localization of a Linear Structure Object in Medical Images Based on Hidden Markov Model","authors":"Tao Xu, Hancheng Xing","doi":"10.1109/CBMS.2003.1212808","DOIUrl":"https://doi.org/10.1109/CBMS.2003.1212808","url":null,"abstract":"","PeriodicalId":74567,"journal":{"name":"Proceedings. IEEE International Symposium on Computer-Based Medical Systems","volume":"205 1","pages":"317-322"},"PeriodicalIF":0.0,"publicationDate":"2003-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87004608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2002-06-04DOI: 10.1109/CBMS.2002.1011389
M. Shifrin, E. E. Kalinina, E. D. Kalinin
This paper is devoted to the MEDSET (Modelling, Engineering, Deployment, Support and Evolution Technology) system for the development, deployment and support of information systems (IS) in clinical medicine and other poorly-formalized subject domains. This technology supports all stages of the IS life-cycle: modelling, engineering, deployment, support and evolution. The focus of the paper is on the evolutionary nature of clinical ISs. MEDSET allows ISs to develop in an evolutionary way, along different "dimensions": the number of supported business processes, the number of user functions, the number of users and the degree of formalization of data. The program component of MEDSET is based on Internet technology, so ISs built under MEDSET can be used in a remote mode as well as locally. The development of the EPR/NSI (Electronic Patient Records for a NeuroSurgical Institute) system for the N.N. Burdenko Neurosurgical Institute, Moscow, Russia is briefly described.
{"title":"MEDSET -- an Integrated Technology for Modelling, Engineering, Deployment, Support and Evolution of Information Systems","authors":"M. Shifrin, E. E. Kalinina, E. D. Kalinin","doi":"10.1109/CBMS.2002.1011389","DOIUrl":"https://doi.org/10.1109/CBMS.2002.1011389","url":null,"abstract":"This paper is devoted to the MEDSET (Modelling, Engineering, Deployment, Support and Evolution Technology) system for the development, deployment and support of information systems (IS) in clinical medicine and other poorly-formalized subject domains. This technology supports all stages of the IS life-cycle: modelling, engineering, deployment, support and evolution. The focus of the paper is on the evolutionary nature of clinical ISs. MEDSET allows ISs to develop in an evolutionary way, along different \"dimensions\": the number of supported business processes, the number of user functions, the number of users and the degree of formalization of data. The program component of MEDSET is based on Internet technology, so ISs built under MEDSET can be used in a remote mode as well as locally. The development of the EPR/NSI (Electronic Patient Records for a NeuroSurgical Institute) system for the N.N. Burdenko Neurosurgical Institute, Moscow, Russia is briefly described.","PeriodicalId":74567,"journal":{"name":"Proceedings. IEEE International Symposium on Computer-Based Medical Systems","volume":"105 1","pages":"277-281"},"PeriodicalIF":0.0,"publicationDate":"2002-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82235821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The clinical practice of diagnostic imaging has been profoundly affected by the digital revolution. The Baltimore V A Medical Center has the longest experience with filmless rJdiology in the United States and has functioned as a demonstration site and clinical laboratory for the Department of Veterans Affairs in the evaluation of the impact of lilmless radiology. The PACS has been used as a tool to redesign workflow, image acquisition, display, and processing. These changes have resulted in major improvement.. in patient care and have changed the practice of diagnostic imaging in a number of interesting and in many cases, unexpected ways.
{"title":"The Changing Face of Clinical Practice: The Digital Revolution","authors":"E. Siegel","doi":"10.1109/CBMS.2001.10000","DOIUrl":"https://doi.org/10.1109/CBMS.2001.10000","url":null,"abstract":"The clinical practice of diagnostic imaging has been profoundly affected by the digital revolution. The Baltimore V A Medical Center has the longest experience with filmless rJdiology in the United States and has functioned as a demonstration site and clinical laboratory for the Department of Veterans Affairs in the evaluation of the impact of lilmless radiology. The PACS has been used as a tool to redesign workflow, image acquisition, display, and processing. These changes have resulted in major improvement.. in patient care and have changed the practice of diagnostic imaging in a number of interesting and in many cases, unexpected ways.","PeriodicalId":74567,"journal":{"name":"Proceedings. IEEE International Symposium on Computer-Based Medical Systems","volume":"6 1","pages":"1-"},"PeriodicalIF":0.0,"publicationDate":"2001-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90864986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1999-06-18DOI: 10.1109/CBMS.1999.781258
W. L. Heinrichs
Three themes: Building 3D Geometric Models from Slice Databases Segmentation and Extraction, and Virtual and Physical Modeling Creating an Educational Context with Information Frames The Hidden Curriculum of Surgery: Simulating Manipulations with Instruction Frames Building 3D geometric models from slice databases requires an aligned, (registered) volumetric dataset. Initial visualization of the anatomic region of surgical interest (AROSI), followed by segmentation and extraction of selected structures in each slice produces 2D masks for each anatomic structure. These are stacked to create 3D virtual anatomic models, either surface or volumetric, which can be transformed into physical 3D models by finite element, or other physical modeling algorithms. The methods for building 3D models will be discussed. Examples are: 1. the Lawrence Berkeley National Labs frog 2. the Stanford Visible Female (pelvis) segmentation is the selection of desired structures, and /or suppression of undesired structures prior to rendering extraction of selected structures in each slice allows for visualization of several types: 3D volumetric visualization and analysis can be done from unreconstructed images, reformated planes, curved planar reformatting, surface and volume rendering, maximum intensity projection, and shaded surface displays. transforming 3D volumetric models into physical models by finite element, or other physical modeling algorithms provides opportunity for deformations, incisions, etc. an application of such models, instrumented with accelerometers and pressure sensors is the crash testing of Cyber Dummies
{"title":"The Critical Path from Tissue Slices to Surgical Simulation: What Do Surgeons Want?","authors":"W. L. Heinrichs","doi":"10.1109/CBMS.1999.781258","DOIUrl":"https://doi.org/10.1109/CBMS.1999.781258","url":null,"abstract":"Three themes: Building 3D Geometric Models from Slice Databases Segmentation and Extraction, and Virtual and Physical Modeling Creating an Educational Context with Information Frames The Hidden Curriculum of Surgery: Simulating Manipulations with Instruction Frames Building 3D geometric models from slice databases requires an aligned, (registered) volumetric dataset. Initial visualization of the anatomic region of surgical interest (AROSI), followed by segmentation and extraction of selected structures in each slice produces 2D masks for each anatomic structure. These are stacked to create 3D virtual anatomic models, either surface or volumetric, which can be transformed into physical 3D models by finite element, or other physical modeling algorithms. The methods for building 3D models will be discussed. Examples are: 1. the Lawrence Berkeley National Labs frog 2. the Stanford Visible Female (pelvis) segmentation is the selection of desired structures, and /or suppression of undesired structures prior to rendering extraction of selected structures in each slice allows for visualization of several types: 3D volumetric visualization and analysis can be done from unreconstructed images, reformated planes, curved planar reformatting, surface and volume rendering, maximum intensity projection, and shaded surface displays. transforming 3D volumetric models into physical models by finite element, or other physical modeling algorithms provides opportunity for deformations, incisions, etc. an application of such models, instrumented with accelerometers and pressure sensors is the crash testing of Cyber Dummies","PeriodicalId":74567,"journal":{"name":"Proceedings. IEEE International Symposium on Computer-Based Medical Systems","volume":"39 1","pages":"118-"},"PeriodicalIF":0.0,"publicationDate":"1999-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81680424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：